FINAL EXAM
I.
Factorial Designs
a)
Definition
i)
Any experimental design with more than one independent variable
ii)
Example
(1) IV #1
Concrete vs. Abstract words
 2 variables
(2) IV #2
Recall vs. Recognition
 2 types of tests
(a)
Could have two experiments
Experiment #1
Experiment #2
Concrete
Abstract
Recall
1
2
(i)
Problem
1.
Could make one experiment

Factorial Matrix
Concrete
Abstract
Recall
1
2
Recognition
3
4
b)
Factorial Matrix
i)
A row and column arrangement that characterizes a factorial design and shows the
independent variables, the levels of each independent variable, and the total number of
conditions (cells) in the study
(1) Levels of the IV are NOT the same as the conditions
(a)
Example:
(i)
IV level: Concrete
(ii) Condition: Concrete recall
c)
Notation for factorial designs
i)
Simultaneously identifies the number of independent variables and the number of levels of
each variable
ii)
Example
(1) 2 x 2 factorial design
(a)
The two numbers shows how many IVs there are
(b) The first number shows the number of levels of IV #1 (word type)
(c)
The second number shows the number of levels of IV #2 (test type)
(2) 2 x 2 x 2 factorial design
(a)
There are 3 IVs each with 2 levels
(3) 2 x 4 x 3 x 4
(a)
There are 4 IVs, the first has 2 levels, the second has 4 levels, etc.
(4) THE ORDER OF THE NUMBERS DOESN’T MATTER
iii) How to tell how many conditions there are by looking at the notation only
(1) The number of conditions in any factorial can be determined simply by calculating the
product of the numbers in the notation system
(2) Example
(a)
3 x 3 factorial design = 9 conditions
(b) 2 x 2 x 2 factorial design = 8 conditions
d)
Building a factorial matrix with more then 2 IVs
C1
B1
B2
A1
A1B1C1
A1B2C1
Concrete
Abstract
Recognition
1
2
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A2B1C1
A2B2C1
i)
HAS ALL 8 CONDITIONS
ii)
These don’t normally work very well with larger factorial designs
e)
MOST STUDIES HAVE MORE THAN ONE IV AND THUS USE FACTORIAL DESIGNS
f)
Advantages of factorial designs
i)
The effects of the independent variables can be examined separately and in combination
(1) Individual effects of each independent variables  MAIN EFFECTS
(2) Interactions between the independent variables  INTERACTIONS
g)
Main Effects
i)
Refer to whether or not significant differences exist between the levels of an independent
variable and a factorial design
Concrete
Abstract
Recall
1
2
Recognition
3
4
(1) HOW MANY MAIN EFFECTS COULD I HAVE?
(a)
As many as the number of IVs in the study
(b) Example
(i)
Above there are two main effects (word type and test type)
ii)
How to tell if there is a main effect
(1) Steps:
(a)
Combine all the data for each of the levels of each IV
(b) Compare the data between the levels of the IV
Concrete
Abstract
Recall
10
20
Mean=15
Recognition
25
15
Mean=20
(i)
IT SEEMS THAT THERE MAY BE A MAIN EFFECT FOR TEST TYPE
Concrete
Abstract
Recall
10
20
Recognition
25
15
Mean=17.5
Mean=17.5
(ii) IT SEEMS LIKE THERE ISN’T
A MAIN EFFECT FOR WORD TYPE
h)
Interactions
i)
In a factorial design, occurs when the effect of one independent variable depends on the level
of another independent variable
Concrete
Abstract
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 Fall '06
 Friedrich
 Correlation and dependence, Pearson productmoment correlation coefficient, Spearman's rank correlation coefficient, factorial design, Correlational Research

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